Publication detail

Network Supervision via Protocol Identification in the Network

HOLASOVÁ, E. KUCHAŘ, K. FUJDIAK, R.

Original Title

Network Supervision via Protocol Identification in the Network

Type

conference paper

Language

English

Original Abstract

This paper is focused on a comparison of ML (Machine Learning) and DNN (Deep Neural Network) techniques in protocol recognition to support network supervision for further proper handling, e.g., detection of a security incident. The DNN approach uses 11 layers and the ML approach is consisting of 28 mutually different predictive models. Both techniques were performed/compared on a freely accessible dataset containing browsing pcap files for further comparison, e.g., with other approaches. The predictive multiclass models were trained (fitted) to be capable of detecting five network protocols. Both approaches were compared by the achieved accuracy (based on testing and validating data), learning time, and predicting the time point of view. Using the ML approach, we were able to recognize the protocol with an accuracy of 1 and using DNN with an accuracy of 0.97.

Keywords

IT protocols, neural networks, machine learning, protocol recognition

Authors

HOLASOVÁ, E.; KUCHAŘ, K.; FUJDIAK, R.

Released

26. 4. 2022

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-6029-4

Book

Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers

Edition

1

Pages from

470

Pages to

474

Pages count

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf

BibTex

@inproceedings{BUT177734,
  author="Eva {Holasová} and Karel {Kuchař} and Radek {Fujdiak}",
  title="Network Supervision via Protocol Identification in the Network",
  booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General papers",
  year="2022",
  series="1",
  pages="470--474",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf"
}